5 Value-Adding Conversation Intelligence Use Cases for Contact Centers

CX Today | MiaRec

Tatiana Polyakova, COO of MiaRec, joins Charlie Mitchell of CX Today for the CX Today discussion to talk about  five contact center use cases for conversational intelligence solutions. These are:

• Use Case 1 – Capturing the true voice of the customer
• Use Case 2 – Monitoring customer AND agent sentiment
• Use Case 3 – Uncovering the root causes of customer issues
• Use Case 4 – Automating quality management processes
• Use Case 5 – Redacting sensitive customer data

During their in-depth discussion, Charlie and Tatiana delve into the incredible potential of AI-powered conversational intelligence tools to revolutionize both customer experiences (CX) and agent performance. Tatiana offers compelling examples of how MiaRec's innovative solutions are transforming agent training, boosting customer satisfaction and retention, and ensuring compliance adherence. This conversation underscores the immense power of leveraging data-driven insights to optimize contact center operations and drive overall success.

To view the the full interview, please watch the video below:

Read the full interview transcript below:

Charlie: Hello and welcome to CX today. My name is Charlie and today I'm delighted to be joined by Tatiana Poliakova, Chief Operating Officer at MiaRec. Tatiana, thank you very much for joining us today. How are you doing? 

Tatiana: I’m doing great. Thank you, Charlie. Thank you for having me.

Charlie: Well, I'm excited to have you join us to speak about conversational intelligence. It's a very interesting topic within the contact center. More and more service operations are using the technology and before this call we've come up with five use cases for such solutions in the contact center. So I was thinking it would be good to go through those one by one and talk about how they work and the benefits they can possibly bring to contact centers. First up, we thought about how conversational intelligence solution can help to capture the true Voice of the Customer (VOC). What can you tell us about this use case?

Tatiana: Absolutely! This is a great question Charlie. I think first we need to mention what Conversational Intelligence is. Conversational Intelligence is a set of tools analyzing your contact center conversations. It’s no secret that contact centers process huge amounts of data every day. What conversational intelligence does is break down this data and analyze it for you so you can make informative decisions for your business. It helps you improve customer service, boost revenue, and improve operational efficiency. When it comes to the Voice of the Customer, conversational intelligence will analyze call recordings for Customer Sentiment. You will see calls with very negative or maybe just negative or neutral and positive and, hopefully, very positive feedback. Companies use a Sentiment Analysis to gather feedback about customer service, about the brand, and about their company in general so that they can better understand their customer and deliver improved customer service and better customer experience (CX) in general. Traditionally,  voice of the customer is measured through post-call surveys, and it's a great tool to measure customer sentiment, but it does have several disadvantages. First of all, and it's the most obvious one, nobody takes the time to complete these surveys.  Once the call is completed and your issue is resolved, you tend to hurry and disconnect from the call and not complete post-call surveys.  The other issue with post-call surveys is that they tend to gather only negative or only positive feedback with no data in between. You will probably be more eager to complete when you're very angry; you're going to say, ‘Okay. I'm going to leave a very bad review for you guys.’ Or when you are very, very happy because the agent went above and beyond and resolved the issue. You feel you definitely want to give a shout-out to this agent and want to leave the review. There's, again, no data in between and it doesn't provide the whole picture. What conversational intelligence does is gather the most honest feedback from the most useful source, the conversation with the customers themselves.  Not only will you have the most honest feedback, but also very, very timely because you will have it right away after the conversation happens. You will be able to take appropriate action. 

Charlie: A lot of good stuff there. I think the key is you’re not just capturing data from those customers that are really happy or really sad, you're capturing data from everyone. You're getting a much clearer picture of the customer experience. Also, you're doing a much more customer-friendly way, I'm sure. We're all a bit fed up of getting those customer surveys clogging up our spam folders on our email. I think lots of good stuff there. Moving on now to our second use case which is uncovering customer and agent sentiment. Can you tell us a little bit about this one? 

Tatiana: Absolutely. We have started talking a little bit about customer sentiment but I think it's worth mentioning that it's really important to analyze both sides of the conversation meaning agent and customer. Why it is important, if you are analyzing only customer sentiment or assigning one general score of the conversation, it will leave you with a lot of questions. Was the call back because the customer was upset, say, about a delivery issue or because the agent was rude? You wouldn't know. If you're analyzing most customer and agent sentiment you will get more insight of what was happening on the call. A lot of times a customer would call about an issue not related to customer service but rather issues, again, with the product or delivery or something. In order to discover how the agent was able to handle the call, we will need to look at both customer and agent sentiments. It gives a business great visibility on how you can accept how their agents are performing in regard of sentiment and again, take appropriate actions like setting up some coaching and training for your agent to handle that kind of situations better.

Charlie: Again, I think that's a some very important points there and also one thing for me, which is really great about the solutions that monitor agent sentiment, is it's such a useful tool for contact center managers in in supporting their teams. If they see agent sentiment is really low for a sustained period of time they can quickly step in have a little one-to-one, offer them a little break or something make sure they are re-energized and in a better position to support customers. That's the use case two that I really like. We can talk a lot about the various insights to conversational intelligence drawn from customers. There are so many of them and that's our next, our third use case, gathering even more customer insight. What can you tell us about this one? 

Tatiana: It's just another great question. Actually, it is my favorite use case because I think conversational intelligence tools can do so much for businesses here. What do I mean by that? Let's talk about from technical standpoint, conversational intelligence solution will look for certain keywords and phrases inside your conversations. Conversation intelligence is able to organize those calls by categories and those categories, called Topics, and the process of this is called topical analysis. Again, if you have millions of calls, this is a huge amount of data, and if it's not organized by any parameter, you cannot do much about it. What Topics and Topical analysis allow you to do is categorize and organize those calls by categories. Let's say it can be a competitor mentions or order cancellations whatever business goals you have and you're trying to achieve with conversational intelligence tools you can customize this tool to allow you to achieve those those goals. Let's say, again, you're trying to understand why you have an uptick in order cancellations. You will look at all the calls that are categorized by conversational intelligence as order cancellation and you will have your team team to listen to those calls to see what's happening. Why do we have all the cancellations? Or, if you have a topic that is called competitor mention, you will know why your customers are leaving you for your competitor. What company, what business wouldn't want to know and actually gather those insights into what's happening with the business?  Maybe you're in your website is not working and you don't know until your customers start calling you, saying I'm trying to order something as a present for Christmas and I cannot do that because your website is not working. Or, there is an issue with the website, I cannot place my order. Conversational intelligence and the specific feature conversation intelligence full topics will allow you again to organize those calls so that you can see what's happening. Also, what's important is that you can see what's happening over time. For example, during a certain period of time, you will see that certain topics are trending. Let's say you see a huge increase in order cancellation topics or in customer service topics. The customer service topic will allow you to see how great your agents are doing and combining this customer sentiment and the agent sentiment that we talked about, it's a great tool because you will know what your customers feel about you and also why they feel that way. You will know they are upset because your website is not working or they are upset because your agents are not handling the conversations well.  Again, it really gives you the full visibility of what is happening with your business and contact center agent performance. It's highly customizable you can configure it and customize it the way you want this this tool to work.

Charlie:  I think for me,  this is my favorite use case just because of the many applications that stem from it. As you say,  you can categorize calls by intent, you can find out the root cause so you know your biggest demand drivers and what's driving them, and then you can apply a conversational AI to them. You can solve some issues that are happening in other departments, you can cut down the demand that comes into the contact center and potentially make huge savings in contact centers. There are so many other things that you can do. You can notice which types of calls may be coming through and maybe you can do something about that such as, if you notice a network is down in a certain area you can you can send a proactive message out to customers in that area so they don't all call in at once. It’s a technology that you can do so much with and I really love that use case. Another one that I really love is our next one and that is Auto QA. Can you tell us, for those who don't know much about automated quality assurance, could you give us a little bit about that?

Tatiana: What I love about conversational intelligence tools is that they not only provide you with full visibility and customer insights but also it can help you automate and scale your quality management processes. What I mean by that, let's look at QA or quality management process in a contact center. Normally it's supervisors or contact center managers are tasked to listen and grade the call, to grade agent performance. When they're listening to calls it can be a one-hour call, it can be more than that, but hopefully not. Then they will have to take time to manually complete the scorecards and basically answer some questions, like, did the agent say thank you for calling, did the agent provide his name or introduce himself and the company? What's more important, did the agent provide the customer with the disclaimer that this call is being monitored and recorded? This is done manually and when the supervisor listens to this call and then goes through scorecards it's a very time-consuming and tedious process. It can and should be automated because even if they spend so much time and it costs a lot of money in labor costs, they still are only able to analyze two to five agent interactions. Moreover, manual evaluations are prone to human bias and errors. If you're evaluating only a fraction of your agent calls, you won’t know what's happening in your contact center. You're basically flying blind in terms of speech adherence and compliance as well which is a very important issue. With Auto QA, on another hand, conversation intelligence platform will automatically transcribe and score 100% of calls for you. Using Auto QA with sentiment analysis will allow you to have full visibility into agent performance. Auto QA alone will save you tons of time and money and also eliminate human bias. It will allow you to see what's very important and the script adherence is this map for all the calls and you will sleep much better at night knowing that the agent did say the required disclaimer and did comply with legal requirements.

Charlie: So our fifth use case that we have is a data redaction. This is a particularly exciting one for contact centers right now. Tatiana, could give us a little bit of insight into how conversational intelligence enables data reduction? 

Tatiana: I think we started talking about automating compliance processes in your contact center and Auto QA is a great start in terms of script adherence compliance, but let's talk about compliance and all privacy laws for a second. What happens if you need your customers to recite their birth date or credit card information or any other sensitive information? Maintaining your customer data privacy right now, I think it's more important than ever. The fines are really huge and what's more, if you don't comply that can result in a damaged reputation and high customer churn. That's something that businesses would want to avoid. Customers do trust you to secure this information. How it’s achieved traditionally, we all know those call recordings, those transcripts do have private information. In order to comply with the laws and reap the benefits of conversational attention tools like MiaRec and actually be able to work with this data, to analyze this data, you will need sensitive information to be removed from a call recording and transcripts. They will probably, in a contact center environment, have agents pause and resume conversations. When the customer is providing some sensitive information like a credit card number, the agent will manually hit pause and then resume the conversation. Once the customer is done providing this credit card information, in our example. Or there is another option of doing that, it's actually having QA scrub the sensitive data after the calls. Neither of those methods is scalable and also they are very prone to human error and time-consuming. It costs a lot of money in labor costs. It's a lot of time and at the end of the day, you still you're still not compliant because it's really, really hard to scrub all this data from the recordings and transcripts given the volume of calls in contact center. The better way of doing that is using AI tools and conversational intelligence tools like MiaRec to your advantage because what MiaRec Auto Data Redaction does - it automatically removes this sensitive data from your call recordings and your transcripts so you can use this data. You can have this access to all the customer insights and you are able to see what customers are feeling about your brand. You can see the areas for improvement in your operational efficiency and in your contact center performance. All of that is by staying compliant with those regulations.
  

Charlie: I think that's why actually there's an additional point here that I think kind of perpetuates the value of this use case in that more and more contact centers will be applying generative AI technologies in their contact centers so you need to feed those technologies with customer transcripts for them to perform at their best. You cannot put sensitive data into this machine.

 It’s a huge risk for contact centers. It's a brilliant use case and as I said earlier, I'm sure if both of us put our heads together we could come up with so many use cases. It's great to see MiaRec helping to really push this technology and bring it into more contact centers. You know there are other vendors within this space so I was thinking maybe a good place to close this conversation is by asking you, Tatiana, why should contact centers consider partnering with MiaRec for conversational intelligence.

Tatiana:  At MiaRec, we have been working for quite sometime, from 2013, at the contact center market and we have been working with contact centers of many sizes from different verticals, from retail to healthcare and we understand the pain points and use cases and we’re here to help. If you want to know more about use cases or ROI calculations for those use cases that we discussed today .and more, you can visit our website at www.miarec.com.

Charlie: Excellent. I will put a link to the MiaRec website in the description box for anybody who wants to check that out. It's been a great conversation today, Tatiana.  I learned so much from it so thank you very much for joining me and also thank you to everybody for watching. Bye for now.